LangChainGEPA shout out to @bryonkuchML for contributing a PR to the GEPA repo to make it work for LangChain! You can now optimize your LangChain chains Docs: https://
gepa-ai.github.io/gepa/tutorials
/langchain_adapter_pair_sum_product_walkthrough/
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SOFTWARE
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Optimize LangChain chains with GEPA now
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Perplexity enhances Daily Digest with customizable sources and connectors
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Perplexity keeps working on the Daily Digest feature, allowing users to precisely customise from where and which data needs to be pulled from. Memory, web sources, custom instructions and many connectors will be available.
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Claude Code’s “Don’t Stop” Mode: Set Goals, Walk Away, Pay the Bill
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Anthropic just gave Claude Code a "don't stop until it's done" mode.
— God of Prompt (@godofprompt) 30 mai 2026
/goal lets you set a completion condition, walk away, and Claude keeps iterating across turns until the condition is met. Hours. Sometimes days.
The capability is real. So is the bill.
Every turn reprocesses… https://t.co/bGVCrv6YXRAnthropic just gave Claude Code a "don't stop until it's done" mode. /goal lets you set a completion condition, walk away, and Claude keeps iterating across turns until the condition is met. Hours. Sometimes days. The capability is real. So is the bill. Every turn reprocesses
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Hermes Agent Control Room: Manage AI Teams Seamlessly, Eliminating Chaos
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You can now run a Hermes agent team from one control room. Running multiple AI agents usually turns into chaos. Credentials scattered everywhere, no clear ownership, nobody knows what runs where. Hermes Agent Control Room is a public template that fixes this. It treats
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Explain vs. Describe: Understanding the Nuance in AI Concepts
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"Explain" vs. "Describe" These feel identical. They're not. "Explain RAG to me" gets you retrieval mechanics, why chunks are embedded, how context windows are populated, and where the architecture breaks. "Describe RAG to me" gets you a surface-level overview. What it looks
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Spinning up a crabbox with webvnc for a test environment
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I ask it to spin up a crabbox with webvnc for a fully prepped test env so I can click through it myself or as Mantis for videos.
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Codex review: all good until told there is a bug
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I do this with codex all the time. Ask it to review code for bugs and it will tell you all good, tell it there is a bug and it will LOOP AND LOOP and will find issues.
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Old model vs new: from syntax to plain language
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The old model was simple:
you had an idea,
then translated it into syntax,
then fought the tooling until it worked. The new model looks very different: → describe the app in plain language
→ generate the interface, logic, and structure
→ test it
→ refine it through feedback -
AI-coded ForgeTrain framework trains MiniCPM-5 1B 10% faster than NVIDIA Megatron
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And here’s the part that feels like foreshadowing: MiniCPM-5 1B was pre-trained with ForgeTrain, an AI-coded training framework claimed to be 10% faster than NVIDIA Megatron. AI isn’t just running apps now. It’s starting to build the factory. Try it. Break it. Contribute:
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Low VRAM AI deployment numbers enable real workflows everywhere
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The deployment numbers are the real flex: FP16: ~2GB VRAM
INT8: ~1GB
INT4/Q4: ~0.5GB That puts real AI workflows on normal machines, edge boxes, tablets, browsers, and local dev stacks. Less “enterprise AI theater.” More shipping.